API

`gopher.read_encyclopedia(proteins_txt)`

Read results from EncyclopeDIA.

PARAMETER	DESCRIPTION
`proteins_txt`	The EncyclopeDIA protein output. TYPE: `str`

RETURNS	DESCRIPTION
`DataFrame`	The EncyclopeDIA results in a format for gopher.

`gopher.read_metamorpheus(proteins_txt)`

Read results from Metamorpheus.

PARAMETER	DESCRIPTION
`proteins_txt`	The Metamorpheus protein output file. TYPE: `str`

RETURNS	DESCRIPTION
`DataFrame`	The Metamorpheus results in a format for gopher.

`gopher.read_diann(proteins_tsv)`

Reads a DIANN-generated TSV file (pg_matrix).

Also processes it, and returns a cleaned Pandas DataFrame.

The function: - Extracts the first protein accession from the “Protein.Ids” column to use as the DataFrame index. - Renames the index axis to “Protein”. - Drops unnecessary metadata columns.

PARAMETER	DESCRIPTION
`proteins_tsv`	Path to the DIANN-generated TSV file. Expected columns: ‘Protein.Group’, ‘Protein.Ids’, ‘Protein.Names’, ‘Genes’, ‘First.Protein.Description’, TYPE: `PathLike`

RETURNS	DESCRIPTION
`pd.DataFrame: A DataFrame with the processed protein data, indexed by`	the first protein accession. The returned DataFrame has the “Protein.Ids” column as the index and all columns are the MSR columns.

`gopher.test_enrichment(proteins, desc=True, aspect='all', species='human', release='current', go_subset=None, contaminants_filter=None, fetch=False, progress=False, annotations=None, mapping=None, aggregate_terms=True)`

Test for the enrichment of Gene Ontology terms from protein abundance.

The Mann-Whitney U Test is applied to each column of proteins dataframe and for each Gene Ontology (GO) term. The p-values are then corrected for multiple hypothesis testing across all of the columns using the Benjamini-Hochberg procedure.

PARAMETER	DESCRIPTION
`proteins`	A dataframe where the indices are UniProt accessions and each column is an experiment to test. The values in this dataframe should be some measure of protein abundance: these could be the raw measurement if originating from a single sample or a fold-change/p-value if looking at the difference between two conditions. TYPE: `DataFrame`
`desc`	Rank proteins in descending order? TYPE: `bool` DEFAULT: `True`
`aspect`	The Gene Ontology aspect to use. Use “cc” for “Cellular Compartment”, “mf” for “Molecular Function”, “bp” for “Biological Process”, or “all” for all three. TYPE: `(str, {cc, mf, bp, all})` DEFAULT: `'all'`
`species`	The species for which to retrieve GO annotations. If not “human” or “yeast”, see here. TYPE: `str, {"human", "yeast", ...}, optional.` DEFAULT: `'human'`
`release`	The Gene Ontology release version. Using “current” will look up the most current version. TYPE: `str` DEFAULT: `'current'`
`go_subset`	The go terms of interest. Should consists of the go term names such as ‘nucleus’ or ‘cytoplasm’. DEFAULT: `None`
`contaminants_filter`	A list of uniprot accessions for common contaminants such as Keratin to filter out. DEFAULT: `None`
`fetch`	Download the GO annotations even if they have been downloaded before? TYPE: `bool` DEFAULT: `False`
`progress`	Show a progress bar during enrichment tests? TYPE: `bool` DEFAULT: `False`
`annotations`	A custom annotations dataframe. DEFAULT: `None`
`mapping`	A custom mapping of the GO term relationships. DEFAULT: `None`
`aggregate_terms`	Aggregate the terms and do the tree search. TYPE: `bool` DEFAULT: `True`

RETURNS	DESCRIPTION
`DataFrame`	The adjusted p-value for each tested GO term in each sample.

`gopher.get_data_dir()`

Retrieve the current data directory for ppx.

`gopher.set_data_dir(path=None)`

Set the ppx data directory.

PARAMETER	DESCRIPTION
`path`	The path for ppx to use as its data directory. TYPE: `str or pathlib.Path object` DEFAULT: `None`